Overview

Dataset statistics

Number of variables41
Number of observations70372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 MiB
Average record size in memory328.0 B

Variable types

BOOL29
NUM9
DATE3

Warnings

tot_contribution_paid_amt is highly skewed (γ1 = 69.13668969) Skewed
Lifetime Giving is highly skewed (γ1 = 30.32802605) Skewed
tot_ticket_paid_amt has 953 (1.4%) zeros Zeros
tot_contribution_paid_amt has 68563 (97.4%) zeros Zeros
Prelim Capacity has 30829 (43.8%) zeros Zeros
Lifetime Giving has 58829 (83.6%) zeros Zeros
days_to_donation has 58782 (83.5%) zeros Zeros

Reproduction

Analysis started2020-09-08 20:03:36.337975
Analysis finished2020-09-08 20:04:59.267828
Duration1 minute and 22.93 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

owner_no
Real number (ℝ≥0)

Distinct38522
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022525.081
Minimum111
Maximum2419301
Zeros0
Zeros (%)0.0%
Memory size549.8 KiB
2020-09-08T16:04:59.468712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile397258.05
Q11928112
median2370216.5
Q32391456
95-th percentile2413551.9
Maximum2419301
Range2419190
Interquartile range (IQR)463344

Descriptive statistics

Standard deviation629695.9448
Coefficient of variation (CV)0.3113414765
Kurtosis2.187826958
Mean2022525.081
Median Absolute Deviation (MAD)40994.5
Skewness-1.835938465
Sum1.42329135e+11
Variance3.965169829e+11
MonotocityIncreasing
2020-09-08T16:04:59.786550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2335299660.1%
 
1485790640.1%
 
275098480.1%
 
2289267470.1%
 
2373627470.1%
 
2140595460.1%
 
2133270440.1%
 
2375331430.1%
 
2388599430.1%
 
2387926420.1%
 
Other values (38512)6988299.3%
 
ValueCountFrequency (%) 
1113< 0.1%
 
2581< 0.1%
 
2622< 0.1%
 
2675< 0.1%
 
8533< 0.1%
 
ValueCountFrequency (%) 
24193011< 0.1%
 
24192781< 0.1%
 
24192481< 0.1%
 
24191261< 0.1%
 
24190111< 0.1%
 
Distinct2160
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
Minimum2014-01-01 00:00:00
Maximum2020-02-29 00:00:00
2020-09-08T16:05:00.166332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:05:00.492126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tot_ticket_paid_amt
Real number (ℝ)

ZEROS

Distinct1159
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5573702184
Minimum-0.1276523274
Maximum34.33847607
Zeros953
Zeros (%)1.4%
Memory size549.8 KiB
2020-09-08T16:05:00.788975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1276523274
5-th percentile-0.1276523274
Q1-0.1276523274
median0
Q30.8723476726
95-th percentile2.629637945
Maximum34.33847607
Range34.4661284
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.262070333
Coefficient of variation (CV)2.264330405
Kurtosis64.08996281
Mean0.5573702184
Median Absolute Deviation (MAD)0.1276523274
Skewness5.40552422
Sum39223.25701
Variance1.592821524
MonotocityNot monotonic
2020-09-08T16:05:01.039813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.12765232743408548.4%
 
0.127652327410321.5%
 
09531.4%
 
1.1361057147931.1%
 
0.11488709477611.1%
 
0.75314873177141.0%
 
1.3914103696951.0%
 
0.25530465486751.0%
 
0.51060930976590.9%
 
0.19147849116330.9%
 
Other values (1149)2937241.7%
 
ValueCountFrequency (%) 
-0.12765232743408548.4%
 
-0.1212697111< 0.1%
 
-0.11488709474< 0.1%
 
-0.10850447831< 0.1%
 
-0.102121861914< 0.1%
 
ValueCountFrequency (%) 
34.338476071< 0.1%
 
32.934300471< 0.1%
 
29.155791581< 0.1%
 
28.045216331< 0.1%
 
27.266537141< 0.1%
 

tot_contribution_paid_amt
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct131
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020798045
Minimum0
Maximum5000
Zeros68563
Zeros (%)97.4%
Memory size549.8 KiB
2020-09-08T16:05:01.338639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5000
Range5000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.71554863
Coefficient of variation (CV)21.1379602
Kurtosis6497.137924
Mean2.020798045
Median Absolute Deviation (MAD)0
Skewness69.13668969
Sum142207.6
Variance1824.618095
MonotocityNot monotonic
2020-09-08T16:05:01.700435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06856397.4%
 
1003340.5%
 
102170.3%
 
251830.3%
 
501280.2%
 
201250.2%
 
5970.1%
 
40760.1%
 
2470.1%
 
25032< 0.1%
 
Other values (121)5700.8%
 
ValueCountFrequency (%) 
06856397.4%
 
121< 0.1%
 
1.51< 0.1%
 
2470.1%
 
2.23< 0.1%
 
ValueCountFrequency (%) 
50002< 0.1%
 
25005< 0.1%
 
22501< 0.1%
 
20002< 0.1%
 
15001< 0.1%
 
Distinct2931
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
Minimum2010-01-10 00:00:00
Maximum2020-02-29 00:00:00
2020-09-08T16:05:01.987287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:05:02.553949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1398
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
Minimum1900-01-01 00:00:00
Maximum2020-08-20 00:00:00
2020-09-08T16:05:02.905740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:05:03.243547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

geo_area_desc
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.674103337
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size549.8 KiB
2020-09-08T16:05:03.540377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.497853338
Coefficient of variation (CV)0.8947197612
Kurtosis5.987047599
Mean1.674103337
Median Absolute Deviation (MAD)0
Skewness2.620774255
Sum117810
Variance2.243564621
MonotocityNot monotonic
2020-09-08T16:05:03.719274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
15116872.7%
 
21027714.6%
 
734734.9%
 
324283.5%
 
515032.1%
 
410801.5%
 
64430.6%
 
ValueCountFrequency (%) 
15116872.7%
 
21027714.6%
 
324283.5%
 
410801.5%
 
515032.1%
 
ValueCountFrequency (%) 
734734.9%
 
64430.6%
 
515032.1%
 
410801.5%
 
324283.5%
 

Prelim Capacity
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.910319445
Minimum0
Maximum10
Zeros30829
Zeros (%)43.8%
Memory size549.8 KiB
2020-09-08T16:05:03.950141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.946138595
Coefficient of variation (CV)1.018750345
Kurtosis-1.09127187
Mean1.910319445
Median Absolute Deviation (MAD)2
Skewness0.4400920866
Sum134433
Variance3.787455429
MonotocityNot monotonic
2020-09-08T16:05:04.169017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
03082943.8%
 
41162216.5%
 
31104315.7%
 
266729.5%
 
552717.5%
 
129974.3%
 
616112.3%
 
71810.3%
 
81300.2%
 
913< 0.1%
 
ValueCountFrequency (%) 
03082943.8%
 
129974.3%
 
266729.5%
 
31104315.7%
 
41162216.5%
 
ValueCountFrequency (%) 
103< 0.1%
 
913< 0.1%
 
81300.2%
 
71810.3%
 
616112.3%
 

ltv_tkt_value
Real number (ℝ)

Distinct2560
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.201675607
Minimum-0.2779850746
Maximum78.66231343
Zeros59
Zeros (%)0.1%
Memory size549.8 KiB
2020-09-08T16:05:04.446859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.2779850746
5-th percentile-0.2779850746
Q1-0.2779850746
median0
Q30.7220149254
95-th percentile6.699626866
Maximum78.66231343
Range78.94029851
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.525258255
Coefficient of variation (CV)3.765790224
Kurtosis94.91915165
Mean1.201675607
Median Absolute Deviation (MAD)0.2779850746
Skewness8.256546927
Sum84564.31584
Variance20.47796228
MonotocityNot monotonic
2020-09-08T16:05:04.730696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.27798507462148430.5%
 
-0.2033582097821.1%
 
-0.24067164187581.1%
 
-0.16604477615640.8%
 
-0.20708955225000.7%
 
-0.020522388064920.7%
 
0.16604477614760.7%
 
0.091417910454640.7%
 
-0.13246268664520.6%
 
-0.05783582094300.6%
 
Other values (2550)4397062.5%
 
ValueCountFrequency (%) 
-0.27798507462148430.5%
 
-0.27425373132< 0.1%
 
-0.270522388113< 0.1%
 
-0.26865671647< 0.1%
 
-0.26679104481< 0.1%
 
ValueCountFrequency (%) 
78.6623134331< 0.1%
 
61.7947761233< 0.1%
 
57.22014925640.1%
 
55.6865671631< 0.1%
 
39.1455223917< 0.1%
 

Lifetime Giving
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct584
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4568.75912
Minimum0
Maximum3417602
Zeros58829
Zeros (%)83.6%
Memory size549.8 KiB
2020-09-08T16:05:05.019528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile490
Maximum3417602
Range3417602
Interquartile range (IQR)0

Descriptive statistics

Standard deviation106920.769
Coefficient of variation (CV)23.40258399
Kurtosis951.2318377
Mean4568.75912
Median Absolute Deviation (MAD)0
Skewness30.32802605
Sum321512716.8
Variance1.143205085e+10
MonotocityNot monotonic
2020-09-08T16:05:05.327372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05882983.6%
 
1007921.1%
 
256731.0%
 
104880.7%
 
504650.7%
 
202660.4%
 
2002640.4%
 
52040.3%
 
2501930.3%
 
401610.2%
 
Other values (574)803711.4%
 
ValueCountFrequency (%) 
05882983.6%
 
1380.1%
 
1.252< 0.1%
 
1.374< 0.1%
 
2920.1%
 
ValueCountFrequency (%) 
3417602640.1%
 
20610423< 0.1%
 
104230131< 0.1%
 
461460.0815< 0.1%
 
35760116< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69057 
1
 
1315
ValueCountFrequency (%) 
06905798.1%
 
113151.9%
 
2020-09-08T16:05:05.562218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

days_to_donation
Real number (ℝ≥0)

ZEROS

Distinct1172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.239925
Minimum0
Maximum3847
Zeros58782
Zeros (%)83.5%
Memory size549.8 KiB
2020-09-08T16:05:05.770117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1208
Maximum3847
Range3847
Interquartile range (IQR)0

Descriptive statistics

Standard deviation490.9563657
Coefficient of variation (CV)3.224885757
Kurtosis17.90792151
Mean152.239925
Median Absolute Deviation (MAD)0
Skewness4.0617276
Sum10713428
Variance241038.153
MonotocityNot monotonic
2020-09-08T16:05:06.091914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05878283.5%
 
10012481.8%
 
997871.1%
 
1606660.1%
 
277640.1%
 
101580.1%
 
253520.1%
 
704500.1%
 
209490.1%
 
920480.1%
 
Other values (1162)916813.0%
 
ValueCountFrequency (%) 
05878283.5%
 
997871.1%
 
10012481.8%
 
101580.1%
 
10214< 0.1%
 
ValueCountFrequency (%) 
384725< 0.1%
 
38236< 0.1%
 
38167< 0.1%
 
37941< 0.1%
 
378313< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69124 
1
 
1248
ValueCountFrequency (%) 
06912498.2%
 
112481.8%
 
2020-09-08T16:05:06.344768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
60817 
1
9555 
ValueCountFrequency (%) 
06081786.4%
 
1955513.6%
 
2020-09-08T16:05:06.468698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

rolling_tkt_sum
Real number (ℝ)

Distinct4868
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.345111923
Minimum-0.2580645161
Maximum495.5225806
Zeros407
Zeros (%)0.6%
Memory size549.8 KiB
2020-09-08T16:05:06.680578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.2580645161
5-th percentile-0.2580645161
Q1-0.2580645161
median0
Q30.7419354839
95-th percentile9.575322581
Maximum495.5225806
Range495.7806452
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.53092567
Coefficient of variation (CV)5.769842174
Kurtosis426.3247918
Mean2.345111923
Median Absolute Deviation (MAD)0.2580645161
Skewness17.04718958
Sum165030.2162
Variance183.0859496
MonotocityNot monotonic
2020-09-08T16:05:06.985420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.25806451612631237.4%
 
-0.12903225818521.2%
 
-0.19354838717541.1%
 
-0.1354838717151.0%
 
0.18709677426580.9%
 
0.50967741946210.9%
 
0.38064516136010.9%
 
-0.0064516129035640.8%
 
-0.064516129035500.8%
 
0.57419354844620.7%
 
Other values (4858)3828354.4%
 
ValueCountFrequency (%) 
-0.25806451612631237.4%
 
-0.25161290323< 0.1%
 
-0.245161290311< 0.1%
 
-0.24193548397< 0.1%
 
-0.23870967741< 0.1%
 
ValueCountFrequency (%) 
495.52258062< 0.1%
 
493.03225811< 0.1%
 
452.38709682< 0.1%
 
451.61290324< 0.1%
 
451.29032261< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
67715 
1
 
2657
ValueCountFrequency (%) 
06771596.2%
 
126573.8%
 
2020-09-08T16:05:07.184287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70169 
1
 
203
ValueCountFrequency (%) 
07016999.7%
 
12030.3%
 
2020-09-08T16:05:07.280232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70178 
1
 
194
ValueCountFrequency (%) 
07017899.7%
 
11940.3%
 
2020-09-08T16:05:07.390168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
63212 
1
7160 
ValueCountFrequency (%) 
06321289.8%
 
1716010.2%
 
2020-09-08T16:05:07.487114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68361 
1
 
2011
ValueCountFrequency (%) 
06836197.1%
 
120112.9%
 
2020-09-08T16:05:07.583058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70358 
1
 
14
ValueCountFrequency (%) 
070358> 99.9%
 
114< 0.1%
 
2020-09-08T16:05:07.683000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
64861 
1
 
5511
ValueCountFrequency (%) 
06486192.2%
 
155117.8%
 
2020-09-08T16:05:07.784975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70039 
1
 
333
ValueCountFrequency (%) 
07003999.5%
 
13330.5%
 
2020-09-08T16:05:07.905875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
62036 
1
8336 
ValueCountFrequency (%) 
06203688.2%
 
1833611.8%
 
2020-09-08T16:05:08.050792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
35256 
0
35116 
ValueCountFrequency (%) 
13525650.1%
 
03511649.9%
 
2020-09-08T16:05:08.155732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
61875 
1
8497 
ValueCountFrequency (%) 
06187587.9%
 
1849712.1%
 
2020-09-08T16:05:08.281659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70371 
1
 
1
ValueCountFrequency (%) 
070371> 99.9%
 
11< 0.1%
 
2020-09-08T16:05:08.388616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69808 
1
 
564
ValueCountFrequency (%) 
06980899.2%
 
15640.8%
 
2020-09-08T16:05:08.488558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
59247 
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ValueCountFrequency (%) 
05924784.2%
 
11112515.8%
 
2020-09-08T16:05:08.639471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
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Memory size549.8 KiB
1
41201 
0
29171 
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14120158.5%
 
02917141.5%
 
2020-09-08T16:05:08.747391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
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Missing (%)0.0%
Memory size549.8 KiB
0
52118 
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18254 
ValueCountFrequency (%) 
05211874.1%
 
11825425.9%
 
2020-09-08T16:05:08.867345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
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Memory size549.8 KiB
1
43240 
0
27132 
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14324061.4%
 
02713238.6%
 
2020-09-08T16:05:08.962289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
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Missing0
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Memory size549.8 KiB
0
69424 
1
 
948
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06942498.7%
 
19481.3%
 
2020-09-08T16:05:09.055213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
59870 
1
10502 
ValueCountFrequency (%) 
05987085.1%
 
11050214.9%
 
2020-09-08T16:05:09.164154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
54426 
1
15946 
ValueCountFrequency (%) 
05442677.3%
 
11594622.7%
 
2020-09-08T16:05:09.315065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
38198 
0
32174 
ValueCountFrequency (%) 
13819854.3%
 
03217445.7%
 
2020-09-08T16:05:09.426021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68456 
1
 
1916
ValueCountFrequency (%) 
06845697.3%
 
119162.7%
 
2020-09-08T16:05:09.547931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
48723 
1
21649 
ValueCountFrequency (%) 
04872369.2%
 
12164930.8%
 
2020-09-08T16:05:09.688850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68507 
1
 
1865
ValueCountFrequency (%) 
06850797.3%
 
118652.7%
 
2020-09-08T16:05:09.784798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
62351 
1
8021 
ValueCountFrequency (%) 
06235188.6%
 
1802111.4%
 
2020-09-08T16:05:09.887740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
66278 
1
 
4094
ValueCountFrequency (%) 
06627894.2%
 
140945.8%
 
2020-09-08T16:05:10.050642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-09-08T16:04:21.306059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:21.812765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:22.310026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:22.586866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:22.871707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:23.189523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:23.511338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:23.895117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:24.245916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:24.551740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:24.929524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:25.265331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:25.617130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:25.931953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:26.246768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:26.614556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:26.971372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:27.366127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:27.697936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:28.124202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:28.472002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:28.812807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:29.127624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:29.437450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:29.754274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:30.039104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:30.364914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:30.826650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:31.141471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:31.511259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:31.797093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:32.114913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:32.415738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:32.729558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:33.067367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:33.398175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:33.681015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:33.974846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:34.266676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:34.599488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:34.893326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:35.204139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:35.551941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:35.869761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:36.195573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:36.503395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:36.851196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:37.178007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:37.494840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:37.791656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:38.118469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:38.468276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:38.808072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:39.162868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:39.601617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:40.145307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:40.610039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:41.348618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:41.971258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-08T16:04:43.151184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:43.777827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:44.212578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:45.043154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:45.396950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:45.770736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:46.096552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:46.546292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:46.870106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:47.194918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:47.591691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:47.929497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:48.229344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:48.518159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:48.885951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:49.187794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:49.747860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:50.069674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:50.423308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:50.759117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:51.135903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-08T16:05:10.721257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-08T16:05:12.119457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-08T16:05:13.455698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-08T16:05:14.873896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-08T16:04:52.026391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T16:04:57.762691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

owner_noorder_dttot_ticket_paid_amttot_contribution_paid_amtfirst_order_dtfirst_cont_dtgeo_area_descPrelim Capacityltv_tkt_valueLifetime Givingprospect_boarddays_to_donationfirst_cont_orderfirst_cont_afterrolling_tkt_sumchannel_desc_3rd Partychannel_desc_At the Performancechannel_desc_Chatchannel_desc_Default Channelchannel_desc_Emailchannel_desc_Faxchannel_desc_Internal Requestchannel_desc_Mailchannel_desc_Mobilechannel_desc_Onlinechannel_desc_Phonechannel_desc_Telefundingchannel_desc_Walk UpMOS_desc_ExternalMOS_desc_InternalMOS_desc_Ticketingdelivery_desc_Digitaldelivery_desc_Do Not Print Ticketsdelivery_desc_Maildelivery_desc_Will Callfacility_desc_Academy of Musicfacility_desc_Fundraiserfacility_desc_Independence Mallfacility_desc_Otherfacility_desc_Perelmanfacility_desc_Small venue
01112015-10-070.7531490.02015-10-071900-01-01150.4048510.000.0000.18709700010000000001000001100000
11112016-09-160.8808010.02015-10-071900-01-01150.4048510.000.0000.69677400000000001000010001100000
21112018-09-210.3191310.02015-10-071900-01-01150.4048510.000.0000.92258110000000000001000001100000
32582014-09-280.9510100.02014-09-281900-01-01560.0373130.000.0000.28709700010000000001000001100000
42622014-10-020.1595650.02014-10-021900-01-0124-0.1100750.000.0000.17741900000000001000010001100000
52622015-03-170.1595650.02014-10-021900-01-0124-0.1100750.000.0000.61290300000000001000010010100000
62672015-04-16-0.1276520.02015-04-161900-01-01163.0391790.000.000-0.25806500000010000000010001100000
72672016-04-131.7743670.02015-04-161900-01-01163.0391790.000.0000.70322600000000010000100010100000
82672016-05-03-0.1276520.02015-04-161900-01-01163.0391790.000.000-0.25806500000010000000010001010000
92672018-05-098.8080110.02015-04-161900-01-01163.0391790.000.0005.21935500000000010000101000000001

Last rows

owner_noorder_dttot_ticket_paid_amttot_contribution_paid_amtfirst_order_dtfirst_cont_dtgeo_area_descPrelim Capacityltv_tkt_valueLifetime Givingprospect_boarddays_to_donationfirst_cont_orderfirst_cont_afterrolling_tkt_sumchannel_desc_3rd Partychannel_desc_At the Performancechannel_desc_Chatchannel_desc_Default Channelchannel_desc_Emailchannel_desc_Faxchannel_desc_Internal Requestchannel_desc_Mailchannel_desc_Mobilechannel_desc_Onlinechannel_desc_Phonechannel_desc_Telefundingchannel_desc_Walk UpMOS_desc_ExternalMOS_desc_InternalMOS_desc_Ticketingdelivery_desc_Digitaldelivery_desc_Do Not Print Ticketsdelivery_desc_Maildelivery_desc_Will Callfacility_desc_Academy of Musicfacility_desc_Fundraiserfacility_desc_Independence Mallfacility_desc_Otherfacility_desc_Perelmanfacility_desc_Small venue
7036224188662016-02-080.4467830.02012-10-042020-04-2071-0.11007525.002855.0010.03225800000000010000101000100000
7036324188662016-02-12-0.1276520.02012-10-042020-04-2071-0.11007525.002855.001-0.25806500000010000000010001010000
7036424189312018-04-251.4041760.02018-04-252020-05-05250.169776100.00841.0010.51612900000000010000101000100000
7036524189992016-10-01-0.12765220.02016-10-012016-10-0113-0.04664220.00100.010-0.25806500000000010000101000001000
7036624189992017-02-130.6637920.02016-10-012016-10-0113-0.04664220.00100.0100.14193500000000010000101000100000
7036724190112018-09-07-0.12765225.02018-09-072018-09-0725-0.27798550.00100.010-0.25806500000000010000101000001000
7036824191262019-09-19-0.1276520.02019-09-191900-01-01100.8414180.000.000-0.25806500000010000000010001100000
7036924192482017-09-202.6806990.02017-09-202019-12-11240.542910100.00912.0011.16129000000000001000011000100000
7037024192782015-04-132.3934810.02011-04-032003-03-26131.12500075.0099.0001.01612900010000000001000010100000
7037124193012018-01-180.6382620.02018-01-181900-01-0110-0.0541040.000.0000.12903200000000010000100010000010